You are confusing the (sampling) variance of the various estimates,
with the variance of the underlying distribution. The latter is
normalized to one regardless of the technique used to estimate the
sampling variances.

--NW

At 01:58 PM 6/2/2006, you wrote:

Aren't things a little trickier in the oprobit and similar contexts?
Suppose that you are interested in the prediction in the sense of the
category that the model suggests has the highest probability. If the
variance of the linear index is higher, there will be higher
probability associated with "further away" categories potentially
changing the category that the model predicts to be most likely. I
apologize for the foregoing abuse of formal statistical terms.